The Development of Strategies to Teach Listening and Speaking Skills to English Second Language (ESL) Learners in African Society: Insight from the Pinetown District, KwaZulu Natal
Bibliographic record
Abstract
Listening and speaking proficiently in English Second Language teaching is a perennial problem in South Africa. While scholars in the domain of ESL acknowledge that there is a severe challenge with teaching listening and speaking skills, there is a shortage of literature in this sphere. Although English is not the home language for most Black learners in South Africa, they are compelled to use English as the Language of Learning and Teaching (LoLT). This serves as a hindrance in developing learner’s proficiency in listening and speaking, which is further exacerbated by poor ESL teaching performed by teachers whose own ESL proficiency is limited. This paper seeks to explore the strategies used by teachers to teach listening and speaking skills to ESL Grade eleven learners’ in selected township schools in the Pinetown District, KwaZulu Natal. Township, in South Africa, refers to racially segregated and often underdeveloped urban areas created for people of color during the apartheid regime. Data was generated using individual semi-structured interviews with eight participating teachers and observation of classroom lessons and document analysis. A significant finding revealed that the claims made by the ESL teachers about their pedagogical practices tallied with the requirements of the Continuous Assessment Policy Statement (CAPS) and their lesson plans but contradicted the ESL teachers’ actual practice in the classroom.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".